event-streaming
Streaming Data Skill
Expert guidance for real-time and near-real-time data pipelines: continuous stream processing, event-driven architectures, and batch-vs-streaming decisions.
When to Use
Activate when:
- Building or troubleshooting Kafka pipelines (producers, consumers, Connect)
- Implementing stream processing with Flink, Spark Streaming, or Kafka Streams
- Designing event-driven architectures or real-time analytics
- Configuring warehouse streaming ingestion (Snowpipe, BigQuery Storage Write API)
- Creating materialized views or dynamic tables
- Evaluating latency requirements (batch vs streaming)
- Handling schema evolution, exactly-once semantics, or idempotent processing
- Debugging consumer lag, backpressure, or checkpoint failures
Do NOT use for: batch ETL (use dbt-transforms), static data modeling, SQL optimization on analytical queries, data quality on static datasets, one-time migrations.
Scope Constraints
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